Pressure sensing systems utilized by Flush Air Data Systems (FADS) of a space vehicle, high-speed aircrafts and planetary probes generally consist of pressure ports, pneumatic tubing and pressure transducers. The pressure transducers measure surface air pressure from the pressure ports and provide the pressure data input to the FADS processor, which in turn generates air data parameters like angle of attack, slide slip angle, Mach number, and dynamic pressure. These air data parameters are crucial to the guidance and control system of the vehicle for real time control and gust load alleviation so as to protect the vehicle systems from aerodynamic heating, to carry out gain scheduling and for guiding the vehicle along the desired trajectory. Hence, it is essential to maintain the accuracy of these pressure measurements for controlling the vehicles.
However, inaccuracies in the pressure measurements can occur as a result of faults in the pressure transducers or due to the blockage of pressure ports caused by icing or foreign particles. The blocked pressure ports and the faulty pressure transducers create significant deviation of the estimated air data parameters from their true values, which may ultimately lead to loss of control and failure of the vehicle's mission. The Mishap Investigation Board studying the cause of the X-31 experimental aircraft accident of NASA on Jan. 19, 1995, has reasoned that an accumulation of ice in or on the unheated pitot-static system of the aircraft provided false airspeed information to the flight control computers. This led to a false reading of total air pressure data and caused the aircrafts flight control system to automatically misconfigure for a lower speed. The aircraft suddenly began oscillating in all axes, pitched up to over 90 degrees angle of attack, went out of control and crashed.
Moreover, there are several other reports of sealing of the pressure ports of the air data systems by insects or due to freezing of trapped water in pneumatic tubing during flight. These events may raise concern about the potential for a takeoff with erroneous airspeed indications and the possibility of inappropriate crew action, which leads to a high-speed rejected takeoff or loss of situational awareness in flight. Therefore, it is always needed to remove the blocked pressure ports and/or the failed transducers from the FADS computations for enhancing the accuracy and reliability of the air data parameter estimation of the FADS in the space vehicles.
The conventional fault detection and isolation (FDI) techniques utilize artificial intelligence algorithm like neural network, for identifying the blocked pressure ports, which are indirect and complex methods that utilize inverse models. In such existing systems, either single or two pressure transducers are connected to one pressure port, which causes difficulty in isolating port blockages from the pressure transducer failures. Further, powering of the single or two transducers is done using a single power supply, which affects the entire pressure measurement when power supply failures occur.
U.S. Pat. No. 7,257,470 describes about a fault isolation method and apparatus in artificial intelligence based air data systems, which is specific to artificial intelligence based air data systems. Such method is an inverse method that requires as many numbers of neural networks as the number of pressure ports. The method mainly depends on the input parameters of the FADS for fault detection in the pressure ports, and thus it requires an inverse model to solve the pressure measurement failures. Such inverse model computations require the output of the pressure estimation at each pressure port in the FADS, which results in computationally expensive and complex process.
GB Patent 2432914A describes about fault detection in artificial intelligence based air data systems, which is also specific to artificial intelligence based air data systems. This method also makes use of large number of neural networks for air data generation and fault detection of the pressure sensors. The main drawback of prior art systems is that they rely on an inverse model for estimating the pressure values at the pressure ports, which complicates the fault detection and isolation (FDI). Also, the existing systems require extensive validation of statistical characteristics like variance of pressure residuals.
With respect to the conventional methods, numerous neural networks are utilized for achieving air data generation and fault detection of the pressure pots in the pressure sensing system of the space vehicles. However, these methods pertain to the inverse model for fault detection, which is very difficult and complex to process on the FADS. In order to overcome the above mentioned deficiencies of the prior art, an FDI scheme with reduced computational load and suitability for onboard implementation point of view is required. Therefore, it is essential to provide an improved system and method for detecting and isolating faults in pressure ports and pressure transducers of the pressure sensing system.